Panel Data Research Center at Keio University
DISCUSSION PAPER SERIES
DP2016-001
April, 2016

Economic and Time Constraints on Women’s Marriage, Childbirth and Employment,
and Effects of Work-Life Balance Policies
Empirical Analysis Using Japanese Household Panel Surveys
Yoshio Higuchi＊
Kazuyasu Sakamoto＊＊
Risa Hagiwara＊＊＊【Abstract】
This paper investigates the effects of economic and time constraints on women’smarriage, childbirth, and employment. According to our analyses using household panel
surveys, we find the following. (1)Women who graduated from college and live with theirparents or their spouse’s parents have a high likelihood of marriage. Women in full-time
employment and those earning a high hourly wage tend to get married. Regular
employees whose working hours and commuting times are short tend to get married. (2)In regard to continued employment after marriage, the husband’s income has negative
effects but the wife’s hourly wage rate has positive effects on continued female
employment. Women who can easily take childcare leave tend to continue working. (3)The likelihood of childbirth increases with the husband’s time spent on housework andchildcare. (4) A higher husband’s income discourages the wife’s continued employment
after childbirth, but women earning a higher hourly wage rate are more likely tocontinue working after giving birth. In addition, the likelihood of continued employment
after childbirth is higher among women in regular employment compared with
non-regular employment. Long working hours and long commuting times discourage
women from continuing to work after childbirth, while childcare leave and theavailability of childcare facilities have positive effects. (5) The more time the husband
spends on housework and childcare, the more likely the wife is to return to work afterchildbirth, though the wife is less likely to do so when the husband’s income is higher.
Focusing on differences between birth cohorts of women, young cohorts are significantly
less likely to likely to get married but are more likely to continue working, even when

holding equal the above-mentioned economic and time constraints and support forwork-life balance. The likelihood of continued regular employment after childbirth ishigh in young cohorts. However, the likelihood of continued non-regular employment is
low among non-regular employees in the young cohorts.

＊

Professor, Faculty of Business and Commerce, Keio University＊＊

Associate Professor, Faculty of Social and Information Studies, Gunma University＊＊＊

Lecturer, Department of Economics, Meikai University

Panel Data Research Center at Keio University
Keio University

Economic and Time Constraints on Women’s Marriage, Childbirthand Employment, and Effects of Work-Life Balance Policies *Empirical Analysis Using Japanese Household Panel Surveys
Yoshio Higuchi ※1
Kazuyasu Sakamoto ※2
Risa Hagiwara ※3
Abstract
This paper investigates the effects of economic and time constraints on women’s marriage, childbirth, andemployment. According to our analyses using household panel surveys, we find the following. (1)Women
who graduated from college and live with their parents or their spouse’s parents have a high likelihood of
marriage. Women in full-time employment and those earning a high hourly wage tend to get married.
Regular employees whose working hours and commuting times are short tend to get married. (2) In
regard to continued employment after marriage, the husband’s income has negative effects but the wife’s
hourly wage rate has positive effects on continued female employment. Women who can easily takechildcare leave tend to continue working. (3) The likelihood of childbirth increases with the husband’s timespent on housework and childcare. (4) A higher husband’s income discourages the wife’s continued
employment after childbirth, but women earning a higher hourly wage rate are more likely to continue
working after giving birth. In addition, the likelihood of continued employment after childbirth is higher
among women in regular employment compared with non-regular employment. Long working hours andlong commuting times discourage women from continuing to work after childbirth, while childcare leaveand the availability of childcare facilities have positive effects. (5) The more time the husband spends onhousework and childcare, the more likely the wife is to return to work after childbirth, though the wife isless likely to do so when the husband’s income is higher. Focusing on differences between birth cohorts
of women, young cohorts are significantly less likely to likely to get married but are more likely to continue
working, even when holding equal the above-mentioned economic and time constraints and support forwork-life balance. The likelihood of continued regular employment after childbirth is high in young cohorts.
However, the likelihood of continued non-regular employment is low among non-regular employees in theyoung cohorts.

Key words:
marriage, childbirth, continued employment, reemployment*

This research used data from the Ministry of Health, Labour and Welfare’s Longitudinal Survey of Adults in the
21st Century and the Japanese Panel Survey of Consumers by the Institute for Research on Household Economics.
We would like to express our sincere gratitude to the Ministry of Health, Labour and Welfare and the Institute for
Research on Household Economics for supplying the data. This research was supported by a Grant-in-Aid for
Scientific Research (2014, policy, general 003) from the Ministry of Health, Labour and Welfare and by the Japan
Society for the Promotion of Science under the research theme “Multi-Dimensional Dynamic Analysis of Gender
Equality and the Role of the Family in Internationally Comparable Data” as part of the Topic Setting Program to
Advance Cutting-Edge Humanities and Social Science Research. Any errors in this report are the responsibility of
the authors alone.
※1
Professor, Faculty of Business and Commerce, Keio University※2
Associate Professor, Faculty of Social and Information Studies, Gunma University※3
Lecturer, Department of Economics, Meikai University

11. Introduction

For women, getting married and having children incurs heavy costs: It limits the amount of time
women are able to use for themselves and constrains their degrees of freedom. If various
constraints prevent women from marrying, having children, or continuing to work despite their
desire to do so, in many cases they will give up on these things. For women, what sorts offactors affect marriage, having children, and continuing to work or reentering the workforce?
According to economic theory, women will choose whether to get married, have children or work
after comparing expected costs and benefits. But what factors constitute these costs andbenefits and what impact does each have? In this paper, we focus on economic and time
constraints. We use household panel surveys, which track the same individuals over an
extended time period, to conduct empirical analysis on the impact of policy measures for easing
constraints on marriage and childbirth, employment continuity, and reentry to the workforce.
By investigating differences among birth cohorts that remain after controlling for financial andtime constraints, we aim to uncover unspecified (including psychological) factors that affecthopes and benefits regarding marriage, childbirth, childcare, and employment such as
education, family environment, and societal environment.
Before moving to our empirical analyses, we first give an overview of recent changes
surrounding women’s marriage, childbirth, and employment using public statistics. The marriage
rate In Japan started declining in 1973, around the time of the first oil shock. After showing slightincreases or level trends from 1988 through 2010, the rate has declined since 2010, albeit
marginally. Over this period, there has been a steady increase in the age at marriage.
Meanwhile, the total fertility rate, which was over 4 immediately after the Second World War,has declined markedly thereafter. From the mid-1950s through the time of the first oil shock,total fertility rate was roughly flat, before again starting to decline, and in 2005 it reached arecord low of 1.26 and has recovered slightly to 1.43 today. However, this is largely due to anincrease in fertility rates among women in their 30s. Due to the shrinking number of women in
their 20s and 30s, the number of babies born each year is on a declining trend. (According topreliminary figures for 2015, the number of births rose from the prior year, albeit only slightly.)
Meanwhile, employment rates for women have been rising recently. According to the Labour
Force Survey by the Ministry of Internal Affairs and Communications, there has been an acrossthe-board increase in female employment rates from 1994 to 2014. This was particularly notablein women aged 25-29 and 30-34 years, which rose by 14.0 percentage points (pp) and 16.0 pp
respectively. A plot of female employment rate versus age traces an M-shaped curve, and its
low point has increased markedly. Nonetheless, as before, from the late 20s through the 30s,
there remains a large decline of roughly 8 pp in the female employment rate (Figure 1).
The National Fertility Survey by the National Institute of Population and Social Security
Research shows how employment patterns have changed for women around the time of major
life events. According to this survey, the percentage of women who keep working around the2time of marriage rose by 4.4 pp from the late 1980s to the late 2000s, and the percentage ofwomen quitting employment upon marriage has declined by 11.7 pp (Figure 2). The number ofwomen continuing to work after marriage is gradually increasing. Next, we examine employment
trends around the time of birth of the first child. As mentioned previously, the number of women
quitting their jobs when they get married has declined, so the share of women not working
before pregnancy has fallen by 11.4 pp. However, the share of women quitting employment due
to childbirth has increased by 6.5 pp, so there has consequently not been any major change inthe share of women continuing to work. The aggregate percentage of women continuing work
after the birth of their first child (the sum of those who take and do not take childcare leave)
remains stuck at around 27%.
To facilitate continued employment of women after life events, the government has establishedproactive measures under the Equal Employment Act and revised the Child Care and Family
Care Leave Act. Companies, too, have taken a number of initiatives. Higuchi (2007) notes a
steady improvement in employment continuity due to the launch of government initiatives tosupport women and the improved operation of existing schemes. Yet, even today, there are nosigns of an end to women withdrawing from the labor market after a life event. The tendency
remains that after the burden of childcare has eased somewhat, they reenter the workforce as
part-time employees. This is not just a matter of making better use of female labor to augment
the workforce as the working age population in Japan declines. In light of the large gap thatremains between the percentage of women who want to work and actual employment rates,
putting in place the social infrastructure so that women can build their own careers while having
and raising children is an important issue in itself.
What sorts of factors are driving these changing circumstances? Why are the desired changes
not progressing much? Below, we elucidate these issues, using panel data from tracking
surveys of the same individuals with further comparisons of differences among cohorts. This
paper is structured as follows. In Section 2, we review previous research analyzing women’smarriage, childbirth, and employment. In Section 3, we review the data used in this research.
Section 4 presents the results of analyzing women’s marriage decisions and Section 5 shows
results of our analysis of changes in women’s employment after marriage. Section 6 presents
an analysis of childbirth decisions, and Section 7 shows the results of analyzing changes in
employment after childbirth. Section 8 reviews estimation results for women reentering theworkforce. The final section presents the conclusions of this research.

3

Figure 1. Female employment rates by age group (1994 vs. 2014)
80

75.7

70.5

70

61.7

60
65.8

50

%

68.0

68.3
60.1

74.4

71.8
68.3

69.8

73.4
66.3

66.3
55.4

51.4

47.6

40

1994

38.6

2014

30
20

15.8

10
0

15.8
14.3

15.6

15-19 20-24 25-29 30-34 35-39 40-44 45-49 50-54 55-59 60-64 over 65

Source: Ministry of Internal Affairs and Communications, Labour Force Survey

Figure 2. Changes in wife’s employment status after birth of the first child by year of marriage
100%
90%
80%

2.0
2.9
1.1
37.3

70%

3.6

3.6

3.4

4.2

4.1
1.0

5.8

7.3

7.7

1.2

1.2

1.5

34.5

31.2

25.6

25.6

100%

80%

60%

50%

50%

40%
30%

40%
56.6

56.9

58.3

3.4

3.8

4.1

5.2

35.5

34.6

32.8

28.5

24.1

37.7

39.3

40.6

43.9

37.4

13

9.7

16.3

11.9

18.3

8.1

11.2

14.8

17.1

5.7
1985-89

1990-94

1995-99

2000-04

2005-09

70%

60%

62.4

3.1

90%

61

30%

20%

20%

10%

10%

0%

0%

1985-89

1990-94

1995-99

2000-04

2005-09

No details

No details
Unemployed before pregnancy
Quit after childbirth
Kept working (no childcare leave)
Kept working (used childcare leave)

Unemployed before marriage
Employed after marriage
Quit when marriedContinued employment

Source: National Institute of Population and Social Security Research (2011) 14th National Fertility Survey:
Marriage and Fertility in Japan, Figure 5-2, 5-3

4

2. Previous research
Since panel data became available, there have been many studies analyzing employmentchanges around the times of marriage and childbirth, starting with Higuchi (2000). Many ofthese studies analyze the combined effects of work related initiatives such as those for childcare
leave, flextime, and reduced working hours, as well as childcare facilities and the husband’s
participation in housework and childcare (time). In this section, we review previous literature,grouping it into research that uncovers positive effects and research that uncovers negative
effects.
First is taking childcare leave. Higuchi (1994), Higuchi et al. (1997), Morita and Kaneko (1998),
Shigeno and Okusa (1998), Wakisaka (2002), Suruga and Zhang (2003), and Noguchi andShimizutani (2004) report that providing childcare leave results in higher rates of employment
continuity after childbirth. Toda (2012) uses the same the Longitudinal Survey of Adults in the
21st Century as our research does and examines the impacts of work-life balance support
measures such as childcare leave on marriage, childbirth, and employment continuity. This work
confirms that childcare leave measures promote continued employment after childbirth. Further,several studies find an effect on women’s employment continuity of providing childcare facilities(Shigeno and Okusa,1999; Nagase, 2003; Higuchi et al., 2007). Some research examines theimpact on childbirth and marriage (Suruga and Nishimoto, 2002; Suruga and Zhang, 2003;
Shigeno and Matsuura, 2003); Shigeno, 2006). These studies show that childcare leavepromotes childbirth. Shimizutani and Noguchi (2004) point out that benefit programs at theworkplace in addition to childcare leave, such as flextime systems, shorter working hours, andin-house childcare facilities promote the participation of married women in the workforce.
Further, with regard to childbirth, Suruga and Nishimoto (2002) note that childcare leave,
promotions during childcare leave, guarantees of promotion and pay upon returning to work,measures to maintain and improve employee skills, and measures to enable staggered startingand finishing times promote fertility. Noguchi (2011) reports that company measures to support
childcare facility use, telecommuting, geographically limited work, and systems to reemploy
workers who have quit to marry or give birth promote fertility. Research by Yoshida andMizuochi (2005) suggests that higher capacity at authorized childcare facilities encourages thebirth of a second child. Regarding the impact of the husband’s housework and childcare activity
on the wife’s participation in the workforce and childbirth, Koba et al. (2009) find that thesefactors increase the wife’s propensity to have children. Yamagami (1999) reports that the more
the husband helps with housework and childcare, the greater the probability that the wife will
work. Mizuochi (2006) points out that the significance of the husband’s participation in childcare
differs depending on whether the wife’s employment status is viewed endogenously or
exogenously. An analysis by Nakano (2009), taking into consideration this endogeneity, showsa clear impact whereby the husband’s participation in housework and childcare promotes thewife’s employment.
Conversely, other research finds no significant impact of work-related measures such as
childcare leave, flextime, and shorter working hours, or of childcare facilities and the husband’s
participation (time) in housework and childcare, or at best the impact is marginally significant.
5

Shigeno and Okusa (2001), Sakatsume and Kawaguchi (2007), and Noguchi (2011) examine
the effect of childcare leave. There is also research on marriage: According to Shigeno andOkusa (1998), childcare leave has no impact on marriage. Specifically, research using
macroeconomic statistics and cohort data comparing periods before and after the introduction ofchildcare leave finds that it has only a small impact on continuing employment (Shigeno andOkusa, 1998; Nagase, 1999; Iwasawa, 2004; Imada and Ikeda, 2006; Shikata and Ma, 2006;
Saito and Ma, 2008; Suga, 2011; Unayama, 2011). According to Suga (2011), since thepromotion of childcare leave and other measures began in order to stem the decline in the birthrate, the younger generation of women has shifted the timing of quitting their pre-marriage workfrom around the time of the marriage to after their first pregnancy. However, the share of womenthat are still working one year after giving birth has not shown any notable increase. In theyoung cohorts, the likelihood of women quitting work during their first pregnancy is particularlyhigh. Unayama (2011) points out that the rate of women quitting work due to marriage andpregnancy was 86.3% from 1980-2005, and that since 1980 it has not changed regardless of
the age at marriage. Further, while the provision of childcare facilities reduces the percentage ofwomen who quit work, childcare leave and living with parents have no significant impact on
employment separation rates. Senda (2002) reports that childcare facilities have no impact on
women continuing to work, at least in the major metropolitan centers of Japan. Yoshida andMizuochi (2005) report that the capacity of authorized childcare centers has no significant
impact on women’s workforce participation. According to Asai et al. (2015), after controlling forspecific prefectural effects (e.g., traditional values), the correlation disappears between theavailability of public childcare services and employment rates. Suruga (2011) notes that thehusband’s housework hours have no impact on the wife’s desire to have children: and that
although it is thought that the husband will increase the time allocated to housework if his
working hours and commuting time become shorter, thus facilitating the wife’s employment,
there is no impact on increasing regular employment. There is a plethora of research regarding
employment changes relating to women’s marriage and fertility, but the results are notnecessarily consistent.
The estimation results of much previous research suggest that few women with high levels of
education find new employment after quitting work to get married or have children (Higuchi,
2000; Hirao, 2005; Sakamoto, 2009). These results are interpreted as follows. Higher
educational attainment among women results in a stronger tendency to be oriented toward
intrinsic rewards—women want their knowledge and experience to be put to use in a job that ischallenging and gives a feeling of accomplishment (Japan Institute of Labour, 2000; Takeishi,
2001). However, either because job openings in the labor market do not meet such criteria, or
because their schooling took so long, these women are late in marrying and having their firstchild. When they are ready to reenter the workforce once the childcare burden is lighter, they
are able to choose from only a limited number of potential jobs. This is the job opening-job
seeker mismatch hypothesis. Further, considering the tendency for women to marry someone ofequal or higher socioeconomic status, highly educated women have a greater likelihood of
having a spouse who is highly educated and earning a high salary, so women’s motivation toearn an income after marriage will not be as strong (weak income motivation hypothesis).
According to Hirao (2005), for female college graduates in particular, there is a strong effect of6

husband’s income on wife’s reemployment. These results regarding the employment of married
women are in line with the first Douglas-Arisawa Law: When the main breadwinner has a highsalary, other household members have low employment rates (Higuchi, 1995; Wakisaka andTomita, 2001).
It is thought that the timing of women’s return to the workforce depends on when their children
become independent. However, detailed research into the careers of 19 women over the age of
35 years via interview surveys by the Japan Institute for Labour Policy and Training (2006) finds
wide discrepancies in the timing of returning to work. For some women, it was before the firstchild had entered elementary school and for some it was not until the youngest child hadentered high school (Okutsu, 2006); the timing depends on the women’s own way of thinking.
Sakamoto (2012) hypothesizes a gendered division of labor attitudes behind the decision not tocontinue work or not to return to work. The idea is that women’s ways of thinking govern theiremployment decisions and they think that the spouses should specialize: the wife should workinside the home, doing housework and raising the children, while the husband should participate
in the labor market to earn an income. Further, Nakamura (2010) points out that women’s
career goals are discernible before women enter the workforce, at the time of university
enrollment. If female students enroll at vocational or liberal arts colleges or colleges withelements of both, this has a major bearing on where they subsequently find employment, as
well as their working careers.
Compared with previous research, our research makes three key advances. First, it uses panel
data. This makes it possible to directly track work changes due to marriage and childbirth for thesame individuals. Second, this research comprehensively analyzes the women themselves
regarding commuting time, wages, husband’s income, childcare services, and the time that thehusband devotes to housework and childcare. Almost all the analyses in previous research
focus on a single factor. There has been little comparative analysis of multiple factors to
examine which have the biggest impact. Third, the present research examines cohort
differences. As explained below, the Longitudinal Survey of Adults in the 21st Century available
for this research spanned 2002-2011, so it was possible to analyze only a single cohort.
However, the Japanese Panel Survey of Consumers has had cohorts added several times since1993. This enables analysis of three different birth cohorts in 10-year intervals and the analysis
of differences among the cohorts.
3. DataIn this research, we analyze the Ministry of Health, Labour and Welfare’s Longitudinal Survey of
Adults in the 21st Century and the Japanese Panel Survey of Consumers by the Institute for
Research on Household Economics. We also employ official statistics (including the
Employment Status Survey and the National Fertility Survey) to supplement these panel data
surveys on women’s employment and perform analysis in line with our aforementioned goals.
The Ministry of Health, Labour and Welfare’s Longitudinal Survey of Adults in the 21st Century
covers men and women who were aged 20-34 years as of the end of October 2002, selected7

from across Japan. The survey consists of two waves: those who were adults in 2002 and thosewere adults in 2012. However, only data for the 2002 wave could be used in this research, so
we have been unable to analyze intergenerational differences. There are two benefits fromusing these data. First, respondents are obliged to answer because these are official
government statistics, so there is a higher response rate and a large sample size in both timeseries and cross-sectional data 1. Second, the Longitudinal Survey of Adults in the 21st Century
includes variables that enable the identification of region (prefecture), allowing matching of
information that indicates regional characteristics such as the availability of childcare facilities.
However, a shortcoming is that there are a limited number of question items because the survey
is for official statistics; there are fewer questions than in the panel data collected by universities
and research institutes.
In this research, we use regional information from the Longitudinal Survey of Adults in the 21stCentury integrated with data from the Ministry of Health, Labour and Welfare’s Survey of Social
Welfare Institutions. Using this survey and population estimates from the Ministry of Internal
Affairs and Communications, we estimate “underlying capacity” as defined by Unayama (2011),
based on the female population aged 25-34 years and childcare facilities, and we then use thisin our analysis. Research prior to Unayama (2011) used childcare facility waiting lists andchildcare facility capacity, but as Unayama (2011) pointed out, these cannot be consideredappropriate for showing the availability of childcare facilities because the number of children
resulting from marriage and childbirth affect these indicators. For example, even if childcare
facilities were insufficient, if marriages and births were declining, then the indicators would
improve and lead to problems such that the provision of childcare facilities would beoverestimated. Conversely, even if childcare facilities were to increase, if the number of people
desiring places also increased as a result, the number of children on waiting lists would tend toincrease. Therefore, in this research, to get an indication of underlying childcare demand,
including from those not yet married, we use “underlying capacity.” Note that in this paper werefer to this underlying capacity as “childcare facility capacity.” Further, as an indicator ofregional labor supply and demand we use the Ministry of Health, Labour and Welfare’s joboffers-to-applicants ratio from the ministry’s job and employment placement service statistics(general employment placement situation) in our estimation.
The Japanese Panel Survey of Consumers by the Institute for Research on Household
Economics started with women who were aged 24-34 years in September 1993 (and men who
were their spouses). Its key characteristics are that many of the questions are aimed at womenand that the survey has been conducted over a long period of time. This continuous survey has
been conducted for over 20 years, and the initial cohort is now aged 45-55 years. It thus covers
not just marriage and childbirth, but subsequent other life events. Further, new respondents
were included as additional samples: women aged 24-27 years in 1997; 24-29 years in 2003;
24-28 years in 2008; and 24-28 years in 2013. The survey has the advantage of followingintergenerational differences. Our research exploits the length of the survey period, and uses
the data primarily to analyze reemployment. Further, we show estimation results for birth cohort
1

However, survey items that needed to be answered by filling in a number such as salary did not necessarily havea high response rate.

8dummies (with those born in the 1960s as the reference group for those born in the 1970s and1980s). This was to capture the effects of age on marriage and childbirth decisions, andcontinued employment after marriage or childbirth. From the next section onward, using thedata discussed, we show the results of analyzing marriage and childbirth decisions and changes
in employment status after marriage or childbirth as well as reemployment after childbirth.
4. Marriage decisions
In this section, we use the Longitudinal Survey of Adults in the 21st Century to examine which
factors have affected marriage decisions since the start of the 2000s. Table 1 shows descriptive
statistics for the sample used in marriage decision estimates. Table 2 shows the results of panel
probit analysis of the data sample in Table 1. We restricted the analysis sample to women who
had not been married the previous year, and the dependent variable took a value of 1 for
women who had married by the next year and 0 for those who had not yet married. In addition
to basic attributes such as age and education, we used various data concerning the workplacein the previous year as explanatory variables.
From Table 2 we can see the following. First, among individual attributes, age and age squared
show positive and negative signs respectively, and are significant. As age increases the numberof women who marry increases, although growth tapers off. Looking at the education dummy,compared with junior high and high school graduates, college graduates have higher marriage
rates (+0.87%). For the living-with-parents 2 variable, there was a significant positive effect in allcases (+1.34% to +2.16%). The results are diametrically opposed to part of the “parasite single”
hypothesis proposed by Prof. Masahiro Yamada in the 1990s. Yamada asserted that living withhigh-income parents was very comfortable for unmarried persons whose parents would pay
housing and living expenses, as the singles could enjoy a lavish lifestyle. Therefore, they would
not choose marriage because living together with a spouse whose income was lower than theirparents would mean that they would be deprived of free time and their luxurious lifestyle. Below
are conceivable explanations as to why our results differ. First is that singles living with theirparents did not necessarily live a “lavish single lifestyle” since the late 1990s due to theeconomic recession. Since the economic downturn of the 1990s, those in their 20s experienced
hardship during the recession, and in an increasing number of cases,” their first job was nonregular employment such as part-time or casual work. They would not be able to achieve
economic independence if they left the family home and so they remained there in an increasing
number of cases (Kitamura and Sakamoto, 2004; Nishi, 2010). Further, their parent’s generation
was not as well off as before, so in an increasing number of households having the children livewith them enabled both sides to support each other’s lifestyles (Kitamura and Sakamoto, 2007).
From these facts, it is clear that singles living with their parents were not in a position to enjoy
one-sided benefits of basic living conditions; they had responsibilities as a member of thehousehold. Further, when the parents started retiring, the children had to take up the household
2

The living-with-parents dummy was a binary variable set at 1 if the respondent lived with their or their spouse’s
parent(s) and 0 if they did not. The form for the Longitudinal Survey of Adults in the 21st Century, instructs
respondents to answer “living together” if the buildings are separate but on the same grounds. Therefore, “living
together” means “living in the same building” or “living on the same grounds” in this research.

9

responsibilities in their stead and had to do the daily cooking and household chores andultimately needed to look after the parents. It is conceivable that living in the family home was afactor pushing them to choose marriage.
We next look at the impact of work-related factors. Commuting times (in the previous year) for
regular employees had a negative and significant impact (-0.09% for every 10 min). For nonregular employees, too, commuting times had a negative sign, though it was not significant.
From this, we confirmed that longer commuting times decreased marriage rates. Commuting
times not only have a fundamentally negative impact on life satisfaction (Asano and Kenjoh,2011), but also cut into the time available to socialize or engage in hobbies, a conceivablereason that workers may not have time to pursue romantic interests.
Meanwhile, looking at working hours and the squared term for working hours, there are both
positive and negative signs for significant cases. Women who work long hours tend to marry,
but as the number of hours increases, the tendency to marry decreases. This reflects the factthat full-time workers are more likely to marry than part-time workers. Next, looking at the
number of employees dummy, compared to workers at firms with 1-4 employees, those with
100-499 employees and those working the public sector are more likely to marry. In all cases,
the access3 to childcare leave failed to show any significant impact. Hourly wage rate showed asignificant positive effect; women with higher wages are more likely to marry (+0.00934% for¥100 per hour).
Further, we conducted analysis taking into consideration when the respondents were born. For
estimates with birth cohort dummies added using Japanese Panel Survey of Consumers data,
for the overall sample and when restricted to regular employees, the sign of the marginal effectis negative for those born in the 1970s and 1980s (compared with those born in the 1960s). In
particular, the 1980s dummy is significant, and when the independent variables are held
constant, the percentage who decide to marry declines for each birth cohort (not shown in thetable).

3

The dummy for accessibility of childcare leave is a binary variable that takes a value of 1 if it was possible to use
childcare leave and the respondent answered, "it is easily accessible in my work atmosphere" and set at 0
otherwise.

10

Table 1 Descriptive statistics for the sample used in the marriage decision estimation
Marriage selection
Marries＝1, does not marry＝0
Age (year before marriage)
Age (year before marriage) squared
Education, ref: junior high/high school
Vocational college
Junior/technical college graduate
Bachelor's degree
Master's degree
Living with parents (year before)
Hourly wage (year before marriage), units: ¥100/hour
Work hours (year before marriage), units: hours/day
Work hours squared (year before marriage), units: hours/day
Commuting time (year before marriage), units: 10 min/return trip
Workplace size (year before marriage), ref: 1-4 workers
5-29
30-99
100-499500-999
1000-4999
5000
Public sector
Accessibility of childcare leave (year before marriage)
Sample size

Total sample
avg.
std. dev.
0.041
0.199
29.3594.870
885.684 290.407
0.337
0.473
0.199
0.399
0.241
0.428
0.209
0.407
0.011
0.105
0.656
0.475
14.922 21.360
7.235
2.832
60.360 40.405
6.154
4.940
0.055
0.228
0.248
0.432
0.172
0.378
0.234
0.424
0.073
0.261
0.098
0.298
0.071
0.257
0.048
0.214
0.143
0.350
25,240Regularavg.
std. dev.
0.053
0.223
28.5894.466
837.261 261.701
0.265
0.441
0.215
0.411
0.269
0.443
0.239
0.426
0.011
0.105
0.720
0.449
17.974 25.083
8.497
2.344
77.693 38.428
6.743
5.055
0.042
0.201
0.219
0.414
0.164
0.370
0.258
0.437
0.077
0.266
0.104
0.305
0.075
0.263
0.061
0.240
0.225
0.417
13,009Non-regular
avg.
std. dev.
0.029
0.168
30.1795.141
937.186 309.924
0.415
0.493
0.181
0.385
0.212
0.409
0.178
0.383
0.011
0.106
0.587
0.492
11.676 15.872
5.892
2.684
41.925 33.749
5.527
4.736
0.068
0.252
0.278
0.448
0.182
0.385
0.209
0.407
0.070
0.255
0.093
0.290
0.067
0.249
0.034
0.181
0.055
0.229
12,231

Source: Ministry of Health, Labour and Welfare, Longitudinal Survey of Adults in the 21st Century

11

Table 2 Marriage decision estimation results (marginal effects)

Dependent variable: marriage
Age (year before marriage)

Total sample
RegularNon-regular
0.0368***0.0448***0.0246***(0.00335)(0.00647)(0.00322)-0.000667*** -0.000812*** -0.000444***(5.84e-05)(0.000114)(5.52e-05)

Age (year before marriage) squared
Education, ref: junior high/high school
Vocational college
Junior/technical college graduate
Bachelor's degree
Master's degree
Living with parents (year before)
Hourly wage (year before marriage), units: ¥100/hour
Work hours (year before marriage), units: hours/day
Work hours squared (year before marriage), units: hours/day
Commuting time (year before marriage), units: 10 min/return trip
Workplace size (year before marriage), ref: 1-4 workers
5-29

0.00545(0.00336)
0.00430(0.00309)
0.00870**(0.00344)
0.00788(0.0123)0.0189***(0.00232)
9.34e-05**(4.42e-05)
0.00487***(0.00157)-0.000139(0.000102)-0.000368(0.000231)
0.00673(0.00609)
0.00235(0.00603)
0.0131**(0.00657)
0.00802(0.00759)
0.0103(0.00741)
0.0134(0.00834)
0.0131(0.00915)
0.00536(0.00329)
25,240-4115

30-99
100-499500-999
1000-4999
5000
Public sector
Accessibility of childcare leave (year before marriage)
Sample size
Log pseudolikelihood

0.00604
0.00250(0.00585)(0.00338)
0.002640.00307(0.00537)(0.00315)
0.0160***0.00188(0.00619)(0.00334)
0.008320.00454(0.0213)(0.0119)0.0216***0.0134***(0.00396)(0.00258)-8.96e-05
0.000129**(0.000143)(5.41e-05)-0.00233
0.00636***(0.00318)(0.00166)
0.000172-0.000292**(0.000173)(0.000115)-0.000907** -4.54e-06(0.000411)(0.000217)
0.00614(0.0107)0.00144(0.0107)0.0113(0.0110)0.00576(0.0126)
0.0127(0.0130)
0.00934(0.0135)-0.00737(0.0115)0.00544(0.00490)
13,009-2608

Source: Ministry of Health, Labour and Welfare, Longitudinal Survey of Adults in the 21st Century
Note: The upper rows are marginal effects, and lower rows in parentheses are standard errors.
***significant at the 1% level; ** significant at the 5% level; *significant at the 10% level.

120.00517(0.00617)
0.000743(0.00597)
0.0103(0.00709)
0.00683(0.00824)
0.00456(0.00725)
0.0139(0.00956)
0.0359**(0.0164)0.00582(0.00553)
12,231-1480

5. Changes in employment after marriage
In this section, we use data from the Longitudinal Survey of Adults in the 21st Century to
examine employment rates after marriage, and investigate which factors affect changes in
employment status around the time of marriage.
Table 3 shows the percentage of women who were still working one year before marriage, in themarriage year, and one, two and three years after marriage among women who were working
two years before marriage. The table is broken down by education and whether the respondent
lived in metropolitan or regional areas. This shows that employment rates drop from one year
before marriage to the marriage year, but that they rise from the marriage year to one year later.
However, the rates start dropping again from the second year to third year after marriage,
forming a W-shaped pattern.
By educational attainment, the employment rate is roughly 95% in all cases in the year before
marriage with no apparent differences, but differences start appearing after marriage.
Compared to female junior high or high school graduates (67.1%), the decline in employment
rates is relatively small for more highly educated women from the year before marriage to themarriage year: the rate is 77.8% for junior or technical college graduates and 81.2% for thosewith a bachelor’s or master’s degree. Employment rates subsequently increase again, but theincrease is greater for women with higher levels of education than junior high or high school
graduates. The impact of educational attainment remains. The differences based on educational
attainment may be due to differences in the women’s psychological state, but at the same time,
foregone income due to leaving work (opportunity cost) is relatively high. Also, more highly
educated women are more likely to work for companies that provide work-life balancearrangements such as childcare leave with a high utilization rate (Abe, 2005). Therefore, these
women may be able to carry on without quitting their jobs after life events such as marriage andchildbirth.
Next, looking at the urban versus regional comparison, the employment rate year is also almost
the same at 94-95% one year before marriage. However, the rate drops to under 70% for theurban dwellers in the marriage year, while that for regional residents is over 80%, for around a
10 pp difference. Subsequently, the gap shrinks by three years after marriage. Unayama (2011)
has previously reported a gap between urban and regional residents, but there are also
differences in the percentage of women leaving work after life events at the prefecture level; it isrelatively high in major metropolitan areas such as Tokyo and Osaka and relatively low in
prefectures along the Japan Sea.

13

Table 3 Employment rates before and after marriage
Employed two years before marriage
One year before marriage
Year of marriage
One year after marriage
Two years after marriage
Three years after marriage
Sample size

Total Junior high/high school
0.944
0.943
0.763
0.671
0.796
0.729
0.827
0.743
0.782
0.686
358
70

Junior/technical college graduate
0.949
0.778
0.801
0.835
0.784
176

Bachelor's/Master's degree
0.950
0.812
0.832
0.871
0.851
101

Urban Regional
0.937 0.949
0.699 0.805
0.741 0.833
0.790 0.851
0.748 0.805
143215

Source: Ministry of Health, Labour and Welfare, Longitudinal Survey of Adults in the 21st Century
Note: “Urban” = Tokyo, Saitama, Chiba, Kanagawa, Hyogo, Osaka, Kyoto. “Regional” is all other prefectures.
The sample covers only those respondents who answered in all years.
The sample for marriage cases is restricted to those without children.

The above-mentioned differences are readily apparent, but we also conducted probit analysis toconfirm differences in employment separation rates, controlling for other factors. Table 4 shows
descriptive statistics for the sample used for the marriage decision estimation. Table 5 shows
the results of probit analysis using the data in Table 4. The data used in the sample is restricted
to women who were working the year before marriage. For the dependent variable, women who
continued working the year before marriage were assigned a value of 1, and those who quit orchanged jobs were assigned a value of 0. The explanatory variables included basic attributes
such as age and education as well as a variety of data related to the women’s workplaces in theprevious year.
Table 5 shows the following. First, looking at basic attributes, in contrast to the marriage
decision estimates, neither age nor age squared provides significant results. Next, the education
effect shows that for non-regular employees, the higher the education, the greater the
probability of continuing to work. Compared with junior high and high school graduates, women
who graduated from technical high schools and junior and technical colleges had employment
rates of roughly 17.9 pp higher. As mentioned above, the cost of income foregone and an
environment that facilitates continued employment at the original workplace are likely factors.
Further, looking at living with parents, in all cases the marginal effect is negative, but notsignificant, showing that it has no impact on continued employment after marriage.
Further, among work-related influences, commuting time is not significant. In contrast to thedecision on marriage, there are no significant differences in women’s continued employment
based on the length of commuting time. Hourly wages have a positive and significant impact(+0.41% for every ¥100 in the total case), indicating that higher wages encourage continued
employment.
Husband’s income has a negative, significant impact for the total sample and for women in
regular employment; it decreases the wife’s employment continuity rates (-2.26% and -2.83%
for every ¥100). This accords with one version of the Douglas-Arisawa Law, which has beenrecognized since 2002. The dummy for number of employees does not show any significant
results for any of the variables. The job-offers-to-applicants ratio, a proxy for labor demand,
does not yield any significant results. Conversely, looking at work hours, the significant cases
had a positive sign; women who worked long hours one year before marriage continued to work
14

after marriage (+3.88% per hour). Finally, the availability of childcare leave had a significant,positive impact for the total sample and regular employees (+23.1% to +28.5%). The availabilityof childcare leave promoted continued female employment around the time of marriage. It isconceivable that whether measures for work-life balance are in place affects whether womencontinue to work as they may anticipate major life events such as childbirth after marriage.
Further, we examined the impact of birth cohorts. For estimation results from the JapanesePanel Survey of Consumers with the sample restricted to regular employees, the dummy for
those born in the 1970s and 1980s yielded positive and significant results (compared to thoseborn in the 1960s). Holding other independent variables constant, the decision to continue
working after marriage is more likely for the younger generations, but among non-regular
workers, the dummy for those born in the 1980s is negative and significant, so the younger
generations tend not to continue working. Depending on whether or not the woman has regular
employment status before marriage, there is a tendency for an increasing impact on
employment continuity after marriage (omitted in the table).
Table 4. Descriptive statistics for the sample used in estimating employment decisions around the time of
marriage
Employment changes around time of marriage
Work: on leave = 1; not working = 0
Age (year before marriage)
Age (year before marriage) squared
Education, ref: junior high/high school
Vocational college
Junior/technical college
University graduate
Living with parents (marriage year)
Husband's income (marriage year), units: ¥1 million/year
Hourly wage (year before marriage), units: ¥100/hour
Work hours (year before marriage), units: hours/day
Work hours squared (year before marriage), units: hours/day
Commuting time (year before marriage), units: 10 min/return trip
Workplace size (year before marriage), ref: 1-4 workers
5-29
30-99
100-499500-999
1000-4999
5000
Public sector
Job-offers-to-applicants ratio (marriage year)
Accessibility of childcare leave (year before marriage)
Sample size

Total sample
RegularNon-regular
avg. std. dev.
avg. std. dev.
avg. std. dev.
0.580
0.494
0.609
0.489
0.523
0.501
27.9383.573
27.7733.539
28.272
3.626
793.253 206.376 783.826 203.170 812.379 211.869
0.245
0.430
0.229
0.421
0.276
0.448
0.204
0.403
0.205
0.404
0.202
0.402
0.287
0.453
0.292
0.455
0.276
0.448
0.262
0.440
0.272
0.445
0.243
0.430
0.163
0.370
0.160
0.367
0.169
0.375
4.012
1.669
4.109
1.674
3.816
1.645
15.098 13.440 16.779 14.348 11.685 10.614
8.126
2.378
8.587
2.389
7.192
2.066
71.687 43.448 79.433 48.198 55.973 25.303
6.805
5.070
6.708
5.302
7.003
4.569
0.037
0.188
0.045
0.207
0.021
0.142
0.224
0.417
0.221
0.415
0.230
0.422
0.126
0.332
0.134
0.341
0.111
0.315
0.295
0.456
0.316
0.466
0.251
0.435
0.079
0.270
0.071
0.257
0.095
0.293
0.122
0.328
0.132
0.339
0.103
0.304
0.095
0.294
0.081
0.273
0.123
0.330
0.022
0.146
0.066
0.249
0.886
0.168
0.883
0.168
0.893
0.169
0.167
0.373
0.209
0.407
0.082
0.275
736
493
243

Source: Ministry of Health, Labour and Welfare, Longitudinal Survey of Adults in the 21st Century

15

Table 5. Estimation results: employment decisions around time of marriage (marginal effects)

Total sample Regular Non-regular
0.02080.0707-0.0820(0.0678)(0.0857)(0.125)-0.000198 -0.00111
0.00157(0.00118) (0.00150) (0.00215)

Dependent variable: continuing work
Age (year before marriage)
Age (year before marriage) squared
Education ref: junior high/high school
Vocational college
Junior/technical college
University graduate
Living with parents (marriage year)
Husband's income (marriage year), units: ¥1 million/year
Hourly wage (year before marriage), units: ¥100/hour
Work hours (year before marriage), units: hours/day
Work hours squared (year before marriage), units: hours/day
Commuting time (year before marriage), units: 10 min/return trip
Workplace size (year before marriage), ref: 1-4 workers
5-29
30-99
100-499500-999
1000-4999
5000
Public sector
Job-offers-to-applicants ratio (marriage year)
Accessibility of childcare leave (year before marriage)
Sample size
Log pseudolikelihood

0.109**(0.0538)0.100**(0.0499)
0.107**(0.0519)-0.0736(0.0526)-0.0226*(0.0127)0.00412**(0.00207)
0.0388*(0.0231)-0.000391(0.00105)-0.00161(0.00425)

0.0786(0.0665)
0.0628(0.0621)0.0865(0.0636)-0.0588(0.0648)-0.0283*(0.0155)
0.00188(0.00293)
0.0201(0.0352)0.000353(0.00135)-0.00416(0.00507)

0.179*(0.0945)0.179**(0.0897)
0.146(0.0942)-0.0617(0.0944)0.000490(0.0219)0.0134**(0.00528)
0.112(0.0811)-0.00547(0.00610)
0.00237(0.00814)

0.0234(0.107)-0.0171(0.114)-0.0558(0.108)-0.0392(0.123)0.00762(0.115)0.0311(0.118)-0.0698(0.164)-0.140(0.115)0.231***(0.0458)736-472.90.0757(0.117)-0.0649(0.132)-0.0134(0.120)-0.0456(0.146)-0.0467(0.134)0.0822(0.129)-0.0837(0.253)0.0543(0.263)-0.215(0.246)-0.129(0.263)0.101(0.263)-0.103(0.269)-0.170(0.265)-0.276(0.207)0.0251(0.128)243-151.5-0.0927(0.141)0.285***(0.0472)493-307.8Source: Ministry of Health, Labour and Welfare, Longitudinal Survey of Adults in the 21st Century
Note: The upper rows are marginal effects, and lower rows in parentheses are standard errors.
***significant at the 1% level; ** significant at the 5% level; *significant at the 10% level.

166. Childbirth decisions
In this section, we use the Longitudinal Survey of Adults in the 21st Century to examine what
factors affect the decision on whether to have children. Table 6 shows descriptive statistics forthe sample used in the childbearing decision estimation. We conducted probit analysis, with thedependent variable taking a value of 1 for women who gave birth and 0 for those who did not.
As before, the explanatory variables used were basic characteristics of the women themselves
and information regarding their place of employment. We also used childcare facility capacity by
prefecture and information about the husband’s income and hours spent on housework andchildcare. Further, to take into account pre-pregnancy factors, given that the normal gestational
period spans roughly 40 weeks, we used data from two years before childbirth rather than theyear before.
Table 7 shows estimation results from which we draw the following conclusions. First, age andage squared show positive and negative signs, respectively (+5.23%, -0.10% in the total samplecase), and are significant in the total sample and regular employees cases. As a result, the age
effect means that the number of women giving birth increases, but the number of women giving
birth declines after a peak age.
The education dummy is not significant in most cases, but where it is significant, the likelihoodof giving birth is relatively high for graduates of junior and technical colleges (+4.42%)
compared with junior high and high school graduates. Conversely, the sign for women with amaster’s degree is negative, suggesting relatively lower fertility (-2.84%) 4 . We expected apositive result for living with parents because it means there are household resources available
to help with childcare, but there were no significant results.

Next, we turn to information regarding women’s employment. In no case was there was any
significant impact from commuting time. Regarding hourly wages, in the non-regular
employment case, the effect was positive and significant (+0.08% for every ¥100), encouraging
the decision to have a child among women in non-regular employment before childbirth. There
were no significant results for the husband’s income. In many cases workplace size was not
significant, but in the significant cases the sign was always negative. Compared to a small (1-4
employee) workplace, the bigger the company where a woman works, the lower the likelihood ofgiving birth.
Looking at number of children, women who already had one child, and those who had at least
two children, at one year before childbirth were less likely to give birth than those with nochildren (-19.5% and -78.1%, respectively). There were no significant results for childcare
facility capacity. For work hours and work hours squared, there were no significant results in allcases. The availability of childcare leave had positive effects, though not significant. For
husband’s hours spent on housework and childcare on days off in the total sample case and the4

In the regular employment case, the sample with a master’s degree does not exist. This sample is very few; only
0.6% has a master’s degree in the total sample.

17

regular employee case, the result was positive and significant. The longer the husband spent onhousework and childcare on his days off, the more likely the woman was to give birth (+0.31%,+0.51%).
Next, we used the Japanese Panel Survey of Consumers to examine the impact of birth cohort.
The dummy for those born in the 1970s was positive and significant (compared to those born inthe 1960s), suggesting that the likelihood of choosing to have children rises with birth cohort(not shown in the table). It is necessary to consider that there may be an issue with the sample
itself. We analyzed the sample controlling for women aged 26-34 years, but the age of mothers
giving birth is rising, and in recent years, the number of women in this age bracket giving birth isincreasing, and it is possible that this is making it appear that the birth rate is increasing. Rather
than more women in the 1970s birth cohort choosing to have children than the 1960s birthcohort, it may be the case that the likelihood of choosing to have children is rising for richinformation on their late 20s and early 30s in the 1970s birth cohort, the age of the respondents(26-34 years) 5 in the Japanese Panel Survey of Consumers sample used for estimations.
Regarding this point, it will be necessary in the future to refine the analysis using historical data.
Table 6. Descriptive statistics for the sample used in the childbirth decision estimation
Childbirth decision
Gives birth = 1; does not give birth= 0
Age (2 years before childbirth)Age (2 years before childbirth) squared
Education, ref: junior high/high school
Vocational college
Junior/technical college
Bachelor's degree
Master's degree
Living with parents (Two years before childbirth)
Husband's income (two years before childbirth), units: ¥1 million/year
Husband's housework/childcare on days off (two years before childbirth), units: hours/day
Number of children (one year before childbirth), ref: No children
1
2 or more
Hourly wage (2 years before childbirth), units: ¥100/hour
Work hours (2 years before childbirth), units: hours/day
Work hours (2 years before childbirth) squared, units: hours/day
Commuting time (2 years before childbirth), units: 10 min/return trip
Workplace size (2 years before childbirth), ref: 1-4 workers
5-29
30-99
100-499500-999
1000-4999
5000
Public sector
Childcare facility capacity (2 years before childbirth)
Accessibility of childcare leave (2 years before childbirth)
Sample size

Total sample
RegularNon-regular
avg.
std. dev.
avg.
std. dev.
avg.
std. dev.
0.121
0.326
0.176
0.381
0.094
0.292
34.0853.814
32.7024.171
34.7593.433
1176.337 250.787 1086.811 265.047 1219.953 231.413
0.425
0.494
0.266
0.442
0.502
0.500
0.169
0.375
0.214
0.410
0.147
0.354
0.264
0.441
0.314
0.465
0.240
0.427
0.136
0.343
0.206
0.404
0.103
0.304
0.006
0.077
0.009
0.094
0.318
0.466
0.331
0.471
0.313
0.464
4.705
1.889
4.680
1.768
4.717
1.946
3.811782 3.84651
3.663
3.832 3.884348 3.85318
0.081
0.273
0.130
0.337
0.058
0.233
0.263
0.440
0.324
0.468
0.233
0.423
0.656
0.475
0.546
0.498
0.709
0.454
13.62715.48019.73919.41710.65012.077
5.839
2.724
8.126
2.070
4.725
2.271
41.51032.27070.31529.11927.47623.122
4.192
3.521
5.273
4.113
3.665
3.059
0.072
0.259
0.038
0.191
0.089
0.285
0.276
0.447
0.183
0.387
0.322
0.467
0.198
0.398
0.183
0.387
0.205
0.404
0.219
0.414
0.273
0.446
0.193
0.395
0.049
0.216
0.063
0.242
0.042
0.202
0.075
0.264
0.092
0.289
0.067
0.251
0.052
0.222
0.053
0.223
0.051
0.221
0.058
0.234
0.117
0.321
0.030
0.170
9.773
5.167
10.372
5.244
9.481
5.106
0.206
0.405
0.488
0.500
0.069
0.253
1,856
608
1,248

Source: Ministry of Health, Labour and Welfare, Longitudinal Survey of Adults in the 21st Century

5

In estimates with a dummy for birth cohort added, the age distribution for each cohort was taken into account,and restricted to respondents aged 25-34 years for all cohorts.

18

Table 7. Results (marginal effects) for the childbirth decision estimation
Dependent variable: childbirthAge (2 years before childbirth)

Total sample Regular
Non-regular
0.0523**0.105**
0.0250(0.0212)(0.0474)(0.0208)-0.000904*** -0.00180** -0.000462(0.000336) (0.000763) (0.000322)

Age (2 years before childbirth) squared
Education ref: junior high/high school
Vocational college
Junior/technical college
Bachelor's degree
Master's degree
Living with parents (Two years before childbirth)
Husband's income (two years before childbirth), units: ¥1 million/year
Husband's housework/childcare on days off (two years before childbirth),units: hours/day
Number of children (one year before childbirth), ref: No children
1
2 or more
Hourly wage (2 years before childbirth), units: ¥100/hour
Work hours (2 years before childbirth), units: hours/day
Work hours (2 years before childbirth) squared, units: hours/day
Commuting time (2 years before childbirth), units: 10 min/return trip
Workplace size (2 years before childbirth), ref: 1-4 workers
5-29

0.0131(0.0191)
0.0442**(0.0196)0.0233(0.0234)-0.0284*(0.0162)-0.000492(0.0128)-0.00374(0.00347)

0.0274(0.0332)-0.00148(0.00927)

0.000427(0.0180)
0.0419**(0.0197)
0.0372(0.0286)-0.0219*(0.0126)-0.00694(0.0119)-0.00192(0.00300)

0.00313*(0.00161)

0.00505*(0.00270)

0.00191(0.00166)-0.195***(0.0248)-0.781***(0.0532)
0.000290(0.000400)-0.00237(0.00733)
0.000161(0.000547)
0.000591(0.00137)-0.325***(0.0544)-0.774***(0.0636)-0.00131(0.00161)-0.0244(0.0187)
0.00112(0.00106)0.00192(0.00319)-0.141***(0.0240)-0.804***(0.0740)0.000850**(0.000359)-0.00692(0.00826)
0.00106(0.000753)-0.000140(0.00151)

0.0387(0.0455)
0.0473(0.0436)0.00453(0.0449)-0.00476-0.0640*0.00905(0.0237)(0.0373)(0.0232)-0.00883-0.0479-0.000426(0.0239)(0.0431)(0.0240)-0.0150-0.0630-5.68e-05(0.0220)(0.0469)(0.0234)-0.00705-0.04750.0104(0.0280)(0.0377)(0.0378)-0.0408*** -0.0729*** -0.0311***(0.0129)(0.0252)(0.0115)-0.00925-0.0356-0.0102(0.0308)(0.0547)(0.0234)-0.0325***-0.0218-0.0444(0.0212)(0.0454)(0.00838)-6.05e-05-0.00254
0.00100(0.00114)(0.00248)(0.00106)0.01750.04650.00820(0.0163)(0.0294)(0.0232)
1,856
608
1,248
-292.7-105.1-177.030-99
100-499500-999
1000-4999
5000
Public sector
Childcare facility capacity (2 years before childbirth)
Accessibility of childcare leave (2 years before childbirth)
Sample size
Log pseudolikelihood

Note: The upper rows are marginal effects, and lower rows in parentheses are standard errors.
***significant at the 1% level; ** significant at the 5% level; *significant at the 10% level.

7. Changes in employment after childbirth19

In this section, we use the Longitudinal Survey of Adults in the 21st Century to examine
employment rates after childbirth. Table 8 shows data for women who were working two years
before childbirth, regardless of birth order. It shows the percentage of women who were working
one year before childbirth, in the childbirth year, and one, two and three years after childbirth, byeducation and whether they lived in urban or regional areas. Employment rates are roughly 75%
in the year before childbirth and drop sharply to roughly 50% in the childbirth year. However,from one year after childbirth onward they turn upward, climbing to 63%, but even three years
after childbirth, employment levels have not returned to those that prevailed one year before
childbirth.
The data confirm that the decline in employment rates from one year before childbirth to thechildbirth year is lower for highly educated women (roughly 55% at childbirth) than junior high or
high school graduates (around 41% at childbirth). Conversely, the increase in employment rates
from the childbirth year to one year after childbirth is larger for junior high or high school
graduates. Similar to changes in employment after marriage, the gap due to education graduallyshrinks over time. Looking at the urban/regional split, employment rates one year before
childbirth and during the childbirth year are higher for urban areas, but from one year afterchildbirth, employment rates in regional areas overtake those in the urban areas. This accords
with previous research (Unayama, 2011), which noted differences in employment separation
rates by prefecture at the time of marriage or childbirth.
Tables 9 and 10 show employment rates over time following the birth of the first child or asecond or subsequent child. The drop in employment rates from one year before childbirth tothe childbirth year is more pronounced in the case of the first child. In these instances,
employment rates in the childbirth year are around half the levels of the year before. For thesecond and subsequent children, employment rates in the childbirth year are around four-fifthsof the level the year before childbirth.
Table 8. Employment rates before/after childbirth by education and location
Employed two years before childbirthYear before childbirthChildbirth year
1 year after childbirth
2 years after childbirth
3 years after childbirth
Sample size

Total Junior high/high school Junior/technical college
0.755
0.733
0.765
0.505
0.412
0.545
0.554
0.508
0.570
0.590
0.562
0.602
0.631
0.611
0.648
1326
386
596

Bachelor's/Master's degree
0.751
0.541
0.580
0.601
0.609
281

Urban Regional
0.778 0.745
0.518 0.500
0.545 0.558
0.568 0.600
0.593 0.647
396
930

Source: Ministry of Health, Labour and Welfare, Longitudinal Survey of Adults in the 21st Century
Note: “Urban” = Tokyo, Saitama, Chiba, Kanagawa, Hyogo, Osaka, Kyoto. “Regional” is all other prefectures.
The sample covers only those respondents who answered in all years.

20

Table 9. Employment rates after birth of the first child
Employed two years before childbirthYear before childbirthChildbirth year
1 year after childbirth
2 years after childbirth
3 years after childbirth
Sample size

Total Junior high/high school Junior/technical college
0.716
0.717
0.718
0.393
0.277
0.422
0.433
0.326
0.460
0.464
0.386
0.486
0.503
0.440
0.529
763
184
348

Bachelor's/Master's degree
0.706
0.447
0.482
0.503
0.518
197

Urban Regional
0.748 0.700
0.412 0.384
0.460 0.419
0.472 0.460
0.508 0.501
250513

Source: Ministry of Health, Labour and Welfare, Longitudinal Survey of Adults in the 21st Century
Note: “Urban” indicates Tokyo, Saitama, Chiba, Kanagawa, Hyogo, Osaka, and Kyoto. “Regional” indicates
all other prefectures.
Sample covers only those respondents who answered in all years.
Table 10. Employment rates after birth of a second or subsequent child
Employed two years before childbirthYear before childbirthChildbirth year
1 year after childbirth
2 years after childbirth
3 years after childbirth
Sample size

Total Junior high/high high school Junior/technical college
0.808
0.748
0.831
0.657
0.535
0.718
0.719
0.673
0.726
0.762
0.723
0.766
0.805
0.767
0.815
563
202
248

Bachelor's/Master's degree
0.857
0.762
0.810
0.833
0.821
84

Urban Regional
0.829 0.801
0.699 0.643
0.692 0.729
0.733 0.772
0.740 0.827
146417

Source: Ministry of Health, Labour and Welfare, Longitudinal Survey of Adults in the 21st Century
Note: “Urban” indicates Tokyo, Saitama, Chiba, Kanagawa, Hyogo, Osaka, and Kyoto. “Regional” indicates
all other prefectures.
The sample covers only those respondents who answered in all years.

Next, we examine which factors affect the employment status of women one year after childbirthamong those who were working one year before giving birth. Table 11 shows descriptive
statistics for the sample used in the following employment decision estimation. Table 12 shows
the results of probit analysis of the data sample in Table 11. We restricted the sample to women
who were working the year before they gave birth, and the dependent variable takes a value of
1 for women who continued working and 0 for those who did not continue working. In addition to
the women’s basic attributes, we used various data concerning their work, spouses, andfamilies as independent variables in our estimations.
We draw the following conclusions from Table 12. First, looking at the women’s basic attributes,
education do not give significant results regarding their effect on the decision to work after
giving birth to the same extent as choosing marriage or childbirth or choosing to work after
marriage. Continued employment was significantly higher than among regular employees who
graduated from vocational school compared with junior high or highs school. Notably,
commuting time to the company they worked at had a big impact. For the entire sample andwomen in the regular employment, commuting time had a significant and negative impact (1.3% to -1.9% for every 10 min). It was not significant for the non-regular employees, but thesign was also negative. From this, it is apparent that in many cases women who had a longcommute to work took childbirth as an opportunity to quit work. Conversely, the results for

21hourly wage rates are positive and significant, suggesting that higher rates encourage continued
employment (+0.92% to +1.24% for every ¥100).
Looking at results for work hours and work hours squared, there are positive and negative
effects, respectively, for the total sample and for women in regular employment. Women who
were working long hours one year before giving birth were more likely to continue working one
year after giving birth (+10.2% to +20.1%), but as working hours increase, the likelihood ofcontinuing to work tapers off. In all cases the accessibility of childcare leave had a significant,positive effect (+28.6% to +35.6%), so it encourages women to keep working.

We next look at family effects. The impact of the husband’s income is negative and significant,discouraging continued employment by the wife (-3.07% to -4.63% per ¥1 million). The impactof the husband’s income decile on reducing the wife’s employment rates appears to be waningover the long term (Ministry of Health, Labour and Welfare, 2014), but our results confirm thatthe husband’s income is still a factor in the wife’s decision on whether to continue employment
at the time of marriage or childbirth. Turning to living with the parents, the marginal effect ispositive, as was the case with the decision to work after marriage, but there were no significant
results.
We now turn to estimation results using a dummy variable for the birth order of the child. It isfound that women who give birth to a second or third child are more likely to continue working
than those who give birth to their first child. This indicates a strong tendency to continue working
after having a second or third child among women who continue working after having their firstchild. The job-offers-to-applicants ratio in the childbirth year, a proxy variable for labor market
demand, has a positive sign, but there are no significant results. Turning to childcare facility
capacity, we see that the higher it is, the higher the likelihood that the mother will continue towork one year after giving birth for the total sample (+0.99%). This is in line with results fromprevious research showing that the provision of childcare facilities has an effect of womencontinuing employment (Shigeno and Okusa, 1999; Higuchi et al., 2007; Unayama, 2011).
Finally, using the Japanese Panel Survey of Consumers to gauge the impact of birth cohort,with other independent variables held constant, there are differences between regular and nonregular employees in the sign of the marginal effect from the birth cohort dummy: It is positive
for the former and negative for the latter. In particular, for non-regular workers born in the 1980s,
there is a declining tendency to remain in employment after giving birth (not shown in the table).

22

Table 11. Descriptive statistics for the sample used in the employment decision estimation for around thetime of childbirth
Changes in employment around childbirth
Work: on leave = 1; not working = 0
Age (year before childbirth)Age (year before childbirth) squared
Education, ref: junior high/high school
Vocational college
Junior/technical college
Bachelor's degree
Master's degree
Living with parents (childbirth year)
Husband's income (childbirth year), units: ¥1 million/year
Birth order of child; ref: first child
SecondThird or subsequent
Hourly wage (year before childbirth), units: ¥100/hour
Work hours (year before childbirth), units: hours/day
Work hours (year before childbirth) squared, units: hours/day
Commuting time (year before childbirth), units: 10 min/return trip
Workplace size (year before childbirth), ref: 1-4 workers
5-29
30-99
100-499500-999
1000-4999
5000
Public sector
Job-offers-to-applicants ratio (childbirth year)
Childcare facility capacity (childbirth year)
Accessibility of childcare leave (year before childbirth)
Sample size

Total sample
RegularNon-regular
avg. std. dev.
avg. std. dev.
avg. std. dev.
0.597
0.491
0.681
0.467
0.478
0.501
29.5683.426
29.056
3.511
30.2943.172
885.989 202.223 856.523 205.400 927.733 190.399
0.332
0.471
0.307
0.462
0.368
0.483
0.225
0.418
0.245
0.431
0.197
0.399
0.245
0.430
0.241
0.429
0.250
0.434
0.183
0.387
0.198
0.399
0.162
0.370
0.015
0.120
0.009
0.096
0.022
0.147
0.261
0.440
0.272
0.446
0.246
0.431
4.225
1.986
4.051
1.779
4.471
2.227
0.530
0.500
0.570
0.496
0.474
0.500
0.194
0.396
0.192
0.394
0.197
0.399
0.276
0.447
0.238
0.427
0.329
0.471
15.281 16.636 18.262 17.503 11.059 14.334
7.059
2.625
8.139
2.121
5.528
2.509
56.704 33.655 70.735 30.470 36.828 27.392
5.735
5.418
6.037
4.744
5.308
6.235
0.051
0.220
0.040
0.197
0.066
0.248
0.269
0.444
0.186
0.390
0.386
0.488
0.142
0.349
0.158
0.365
0.118
0.324
0.267
0.443
0.319
0.467
0.193
0.396
0.078
0.268
0.093
0.291
0.057
0.232
0.085
0.280
0.096
0.295
0.070
0.256
0.089
0.285
0.108
0.311
0.061
0.241
0.020
0.140
0.048
0.215
0.886
0.169
0.901
0.158
0.866
0.183
10.044
5.196
10.483
5.260
9.421
5.050
0.236
0.425
0.337
0.474
0.092
0.290
551
323
228

Source: Ministry of Health, Labour and Welfare, Longitudinal Survey of Adults in the 21st Century

23

Table 12. Results for the employment decision estimation around the time of childbirth (marginal effects)
Dependent variable: continuing to work
Age (year before childbirth)Age (year before childbirth) squared
Education, ref: junior high/high school)
Vocational college
Junior/technical college
Bachelor's degree
Master's degree
Living with parents (childbirth year)
Husband's income (childbirth year), units: ¥1 million/year
Birth order of child; ref: first child
SecondThird or subsequent
Hourly wage (year before childbirth), units: ¥100/hour
Work hours (year before childbirth), units: hours/day
Work hours (year before childbirth) squared, units: hours/day
Commuting time (year before childbirth), units: 10 min/return trip
Workplace size (2 years before childbirth), ref: 1-4 workers
5-29
30-99
100-499500-999
1000-4999
5000
Public sector
Job-offers-to-applicants ratio (childbirth year)
Childcare facility capacity (childbirth year)
Accessibility of childcare leave (year before childbirth)
Sample size
Log pseudolikelihood

Total sample-0.000617(0.0967)0.000264(0.00165)

Regular0.121(0.109)-0.00194(0.00188)

Non-regular-0.285(0.184)0.00527*(0.00309)

0.0922(0.0621)0.0379(0.0607)0.0620(0.0658)
0.0447(0.194)0.0492(0.0559)-0.0307**(0.0137)

0.112*(0.0674)
0.00419(0.0752)
0.0215(0.0775)
0.00816(0.200)0.0474(0.0649)-0.0373*(0.0191)

0.100(0.107)0.102(0.0982)
0.0841(0.120)-0.0533(0.265)0.0544(0.0957)-0.0463*(0.0248)

0.338***
0.280***(0.0434)(0.0433)
0.290***
0.164**(0.0496)(0.0657)
0.00973*** 0.0124***(0.00211)(0.00337)
0.102***
0.201***(0.0360)(0.0544)-0.00454* -0.00967***(0.00263)(0.00322)-0.0132** -0.0188***(0.00539)(0.00675)

0.394***(0.0856)0.401***(0.0815)0.00918*(0.00516)
0.0224(0.0732)0.001000(0.00643)-0.00450(0.00485)-0.0743(0.114)-0.182(0.124)-0.240**(0.117)-0.0938(0.148)-0.223(0.137)-0.0641(0.140)-0.402**(0.174)0.157(0.154)0.00998**(0.00476)
0.316***(0.0442)551-286.9-0.291**(0.138)-0.292**(0.136)-0.467***(0.102)-0.473***(0.0731)-0.381***(0.117)-0.308**(0.144)-0.456***(0.0814)0.227(0.205)0.0102(0.00828)
0.356***(0.107)228-120.50.0542(0.130)-0.0467(0.150)-0.00842(0.139)0.212***(0.0775)
0.00710(0.153)0.118(0.121)-0.0750(0.214)0.00711(0.00516)
0.286***(0.0480)323-147.6

Source: Ministry of Health, Labour and Welfare, Longitudinal Survey of Adults in the 21st Century
Note: The upper rows are marginal effects, and lower rows in parentheses are standard errors.
***significant at the 1% level; ** significant at the 5% level; *significant at the 10% level.

248. Changes in the timing of reemployment after childbirth-related job separation
In this section, we use the Japanese Panel Survey of Consumers by the Institute for Researchon Household Economics to examine factors that affect reentering the workforce after giving
birth. Figure 3 shows when women return to the workforce after quitting work to give birth (thevertical axis shows cumulative percentage of women who have resumed employment and thehorizontal axis shows the years elapsed until employment resumes). During the survey period,
361 of 719 women who gave birth to their first child quit their job. Subsequently, 29 (8.0%)
returned to work within one year; and a cumulative total of 81 (22.4%) returned to work within
three years; 118 (32.7%) within five years; and 175 (48.5%) within 10 years. Employment rates
for women who quit work to give birth are low in Japan; in the United States, 60% of women
return to work nine months after giving birth (Han et al, 2008). The vast majority of women in
Japan who return to work are in non-regular employment (regular employment, 5.5%; nonregular employment, 70.5%; and self-employed and in family businesses, 19.5%).

As in the analyses described in the previous sections, we looked at differences due to education
and residence. The cumulative share of junior high and high school graduates is high (Figure 4).
The share returning to work within one year is higher for more highly educated women (juniorhigh/ high school graduates, 6.9%; junior/technical college graduates, 8.1%; bachelor’s/master’s
degree holders, 9.3%). Conversely, four years after giving birth, the cumulative share of those
returning to work is lower among the more highly educated, with clear differences 10 years later(56.5%, 47.5%, and 39.5%, respectively). Next, turning to urban versus regional residents, fromone year after giving birth onward, the regional residents have higher cumulative reemployment
rates, with the gap widening over time (Figure 5).
Figure 3. Cumulative reemployment rate after giving the first birth

60
50
40

% 30
20
10
0
1

2

3

4

5

6

7

8

9 10 11 12 13 14 15 16 17 19

years
Source: Institute for Research on Household Economics, Japanese Panel Survey of Consumers

25

Figure 4. Cumulative reemployment rates after giving the first birth by education level
70
60
50

%

40
30

Junior high/high school graduates
Junior/technical college
Bachelor's/Master's degree

20
10
0
1

2

3

4

5

6

7

8

9

10

11

12

13

14

15

16

17

18

19

years

Source: Institute for Research on Household Economics, Japanese Panel Survey of Consumers

Figure 5. Cumulative reemployment rates after giving the first birth by residence location
70
60
50
40

%

Urban

30

Regional

20
10
0
1

2

3

4

5

6

7

8

9

10

11

12

13

14

15

16

17

18

years

Source: Institute for Research on Household Economics, Japanese Panel Survey of Consumers

Next, using a panel probit model, we estimated a reemployment function. The sample used in
the estimation was restricted to women who left work after giving birth to their first child. The
dependent variable took a value 1 for women who subsequently rejoined the workforce and 0
for those who remained out the workforce. As previously mentioned, virtually none of thewomen resumed employment as regular employees, so our estimates look at only two cases:
the total sample and non-regular employees.
We obtained the following conclusions from Table 14. Looking at education, as shown in theprevious figure, junior and technical college graduates have lower probabilities of reemployment
once they quit compared to junior high and high school graduates, in line with the mismatch26

hypothesis. We also confirmed the impact of qualifications held by the women 6; while the sign
was positive, there was no significant impact. Next, we look at the impact of families, in terms ofliving with parents time spent by the husband on housework and childcare. The results
confirmed that the longer the husband spent on housework and childcare on weekend the year
before, the higher the women’s reemployment rates (+0.06% per hour). This indicates that themore cooperative the husband is in domestic life, the easier it is for the wife to participate in thelabor market. The marginal effect of the husband’s income was negative and significant (-0.03%
to -0.04% per ¥1 million). This constrains the wife’s income and satisfies the weak income
motivation hypothesis. Finally, looking at the impact of birth cohort, we see that among women
who had quit work, those born in the 1970s and 1980s were less likely to reenter the workforce(roughly -15% and -70%, respectively) compared with those born in the 1960s. At the time of
the latest survey (2014), the mothers in the 1960s cohort were 45-54 years old, and assumingthat they were around 30 years old when their first child was born, they have already finishedchild rearing. In addition, more of the younger generations who wanted to work kept working.
Restricting the discussion to those who quit, few of them have resumed employment.
Table 13. Descriptive statistics for the sample used in the reemployment estimation

Reemployment decision
Reemployment = 1, no reemployment = 0
Age (year before)Age (year before) squared
Junior high/high school
Vocational college graduate
Junior college graduate
Bachelor's/Master's degree
Holds qualification
Living with parents (year before)
Job-offers-to-applicants ratio (year before)
Has housing loan (year before)
Husband's housework/childcare (year before): hours/day
Husband's income (year before): ¥1 million/year
Born in 1960s
Born in 1970s
Born in 1980s
Sample size

Total sample
avg.
std. dev.
0.089
0.285
33.5574.829
1149.363
340.685
0.314
0.464
0.195
0.396
0.266
0.442
0.226
0.418
0.244
0.429
0.100
0.300
0.749
0.316
0.367
0.482
219.995
205.440548.704
238.801
0.455
0.498
0.448
0.497
0.081
0.273
2028

Non-regular
avg.
std. dev.
0.085
0.279
33.562
4.833
1149.781 340.941
0.315
0.464
0.194
0.395
0.266
0.442
0.225
0.418
0.242
0.429
0.099
0.299
0.750
0.317
0.366
0.482
219.222205.278549.334
239.080
0.456
0.498
0.446
0.497
0.081
0.273
2018

Source: Institute for Research on Household Economics, Japanese Panel Survey of Consumers

6

Qualifications here are defined as any of the items in the list below: medical doctor, dentist, pharmacist, nurse,
public health nurse, dental hygienist, clinical laboratory technician, social worker or care worker, nutritionist, cook,teacher, lawyer, judicial scrivener, administrative scrivener, social insurance consultant, small business
management consultant, certified public accountant, tax accountant, architect, hairdresser, beautician.

27

Table 14. Estimation results for reemployment

(4)
(2)
(3)
(1)
Total sample Non-regular Total sample Non-regular-0.06-0.0537-0.0567-0.0501(0.0997)(0.1010)(0.1010)(0.1020)0.000518
0.000473
0.000540.000484(0.0014)(0.0014)(0.0014)(0.0014)

Dependent variable: reemployment
Age (one year before)Age squared (one year before)
Education, ref: junior high/high school
Vocational college
Junior/technical college
University graduate
Has qualification
Living with parents (year before)
Job-offers-to-applicants ratio (one year before)
Has housing loan (one year before)-0.0373(0.1220)-0.227*(0.1160)-0.148(0.1180)0.1(0.1030)
0.077(0.1310)-0.161(0.1840)0.0482(0.0875)-0.0712(0.1250)-0.239**(0.1170)-0.176(0.1210)0.068(0.1060)
0.043(0.1350)-0.0166(0.1880)0.0237(0.0892)-0.319**(0.1530)-0.703**(0.3110)Yes
2,028-582.9-0.329**(0.1560)-0.696**(0.3170)Yes
2,018-560.7

Husband's housework/child care (one year before): hours/day
Husband's income (year before)
Birth cohort dummy, ref: 1960s
Born in 1980s
Born in 1970s
Year dummy
Sample size
Log pseudolikelihood-0.0394-0.0759(0.1230)(0.1260)-0.233**-0.245**(0.1180)(0.1190)-0.111-0.142(0.1230)(0.1250)
0.06220.034(0.1040)(0.1070)0.08390.0511(0.1320)(0.1360)-0.1050.0313(0.1870)(0.1910)0.07360.0445(0.0890)(0.0907)0.000639*** 0.000583***(0.0002)(0.0002)-0.000369* -0.000315(0.0002)(0.0002)-0.360**(0.1550)-0.760**(0.3130)
Yes
2,028-575.6-0.365**(0.1570)-0.745**(0.3190)Yes
2,018-555.1

Source: Institute for Research on Household Economics, Japanese Panel Survey of Consumers
Note: The upper rows are marginal effects, and lower rows in parentheses are standard errors.
***significant at the 1% level; ** significant at the 5% level; *significant at the 10% level.

9. Conclusions
In this study, we analyzed the impact of economic and time constraints on women’s marriage,
childbirth, and employment behavior, arriving at the following key conclusions. 1) Women who
are university graduates and living with their parents are more likely to marry than others.
Further, full-time employees and those with higher hourly wages have higher marriage rates
than part-time and lower paid employees. Among regular employees, those with shorter
commuting times are more likely to marry. 2) Turning to the rates of continued employment after
marriage, the lower the husband’s salary and the higher a woman’s hourly wage, and the higher
her educational attainment, the higher the rate of continued employment. More women remain
employed after marriage if employees in their company utilize childcare leave and they work incompanies where childcare leave is readily available. 3) More women have children inhouseholds where the husband spends more time on housework and childcare on his days off.
4) Rates of continued employment after childbirth are lower in households where the husband’s
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income is high, and higher when the woman’s hourly wage is high. Among regular employees,
there are higher rates of continued employment for women who had long working hours and therates decline further as commuting times increase. Companies where childcare leave is readily
available and areas with many childcare facilities relative to the number of children have higher
rates of continued employment. Overall, many factors have significant impacts on continued
employment after marriage and childbirth. 5) Among women who quit work to give birth,
reemployment rates are higher in households where the husband spends more time onhousework and childcare. In households where the husband’s income is high, the wife’s
reemployment rates are low.
Next, we summarize the differences among women’s birth cohorts. Holding constant the abovementioned economic and time constraints and the various policies meant to redress suchbarriers, there is a significant decline in marriage rates among the young cohorts and an
opposite rising trend to continue employment after marriage. Meanwhile, looking at childbirth,reflecting increasing birthrates of women in their mid-30s and holding other factors constant, theyounger cohorts tend to have higher birth rates, and rates of continuing employment after giving
birth for regular employees show a significant increasing tendency. For non-regular employees,
the rates show a tendency to decrease. Taken together, these results show how much
independent variables other than economic and time constraints—that is, factors including
psychological differences—have a major impact on the behavior of different cohorts.
For women to get married, have children, and continue working in accordance with their wishes,
it is necessary for these various factors to be aligned. Addressing just one area is insufficient. If
any one of them is lacking, attaining work-life balance becomes difficult.
Further analysis should be done to elucidate in concrete terms other factors uncovered during
the inter-cohort analyses. In this paper, prior behavior was a predetermined variable and treatedexogenously. We attempted to elucidate behavior at different time points by probit analysis. In
the future, it will be necessary to include historical data from before the survey period, and byextending the sample period, to conduct survival analyses to obtain stable analysis results.

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